In a few cases, researchers have deployed deep RL in ways analogous to previous applications of deep learning and RL. For example, transplanting a longstanding research strategy from deep learning [1]}, [2]} to deep RL, [3]} trained a recurrent deep RL model on a series of reward-based decision making tasks that have been studied in the neuroscience literature, reporting close correspondences between the activation patterns observed in the network’s internal units and neurons in dorsolateral prefrontal, orbitofrontal and parietal cortices (Figure REF c). Work by [4]} combined supervised deep learning and deep RL methods to show how grid-like representations resembling those seen in entorhinal cortex can enhance goal-directed navigation performance.
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